python爬取微博评论的实例讲解
(编辑:jimmy 日期: 2024/11/16 浏览:3 次 )
python爬虫是程序员们一定会掌握的知识,练习python爬虫时,很多人会选择爬取微博练手。python爬虫微博根据微博存在于不同媒介上,所爬取的难度有差异,无论是python新入手的小白,还是已经熟练掌握的程序员,可以拿来练手。本文介绍python爬取微博评论的代码实例。
一、爬虫微博
与QQ空间爬虫类似,可以爬取新浪微博用户的个人信息、微博信息、粉丝、关注和评论等。
爬虫抓取微博的速度可以达到 1300万/天 以上,具体要视网络情况。
难度程度排序:网页端>手机端>移动端。微博端就是最好爬的微博端。
二、python爬虫爬取微博评论
第一步:确定评论用户的id
# -*- coding:utf-8 -*- import requests import re import time import pandas as pd urls = 'https://m.weibo.cn/api/comments/show"htmlcode">tags = re.compile('</"htmlcode">def get_comment(url): j = requests.get(url, headers=headers).json() comment_data = j['data']['data'] for data in comment_data: try:第四步:利用正则表达式去除文本中的html标签
comment = tags.sub('', data['text']) # 去掉html标签 reply = tags.sub('', data['reply_text']) weibo_id = data['id'] reply_id = data['reply_id'] comments.append(comment) comments.append(reply) ids.append(weibo_id) ids.append(reply_id)第五步:爬取评论
df = pd.DataFrame({'ID': ids, '评论': comments}) df = df.drop_duplicates() df.to_csv('观察者网.csv', index=False, encoding='gb18030')实例扩展:
# -*- coding: utf-8 -*- # Created : 2018/8/26 18:33 # author :GuoLi import requests import json import time from lxml import etree import html import re from bs4 import BeautifulSoup class Weibospider: def __init__(self): # 获取首页的相关信息: self.start_url = 'https://weibo.com/u/5644764907"accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "max-age=0", "cookie": 使用自己本机的cookie, "referer": "https://www.weibo.com/u/5644764907", "upgrade-insecure-requests": "1", "user-agent": "Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.96 Safari/537.36", } self.proxy = { 'HTTP': 'HTTP://180.125.70.78:9999', 'HTTP': 'HTTP://117.90.4.230:9999', 'HTTP': 'HTTP://111.77.196.229:9999', 'HTTP': 'HTTP://111.177.183.57:9999', 'HTTP': 'HTTP://123.55.98.146:9999', } def parse_home_url(self, url): # 处理解析首页面的详细信息(不包括两个通过ajax获取到的页面) res = requests.get(url, headers=self.headers) response = res.content.decode().replace("\\", "") # every_url = re.compile('target="_blank" href="(/\d+/\w+\" rel="external nofollow" ', re.S).findall(response) every_id = re.compile('name=(\d+)', re.S).findall(response) # 获取次级页面需要的id home_url = [] for id in every_id: base_url = 'https://weibo.com/aj/v6/comment/big"//div[@class='list_li S_line1 clearfix']/div[@class='WB_face W_fl']/a/img/@alt") # 评论人的姓名 info = html.xpath("//div[@node-type='replywrap']/div[@class='WB_text']/text()") # 评论信息 info = "".join(info).replace(" ", "").split("\n") info.pop(0) comment_time = html.xpath("//div[@class='WB_from S_txt2']/text()") # 评论时间 name_url = html.xpath("//div[@class='WB_face W_fl']/a/@href") # 评论人的url name_url = ["https:" + i for i in name_url] comment_info_list = [] for i in range(len(name)): item = {} item["name"] = name[i] # 存储评论人的网名 item["comment_info"] = info[i] # 存储评论的信息 item["comment_time"] = comment_time[i] # 存储评论时间 item["comment_url"] = name_url[i] # 存储评论人的相关主页 comment_info_list.append(item) return count, comment_info_list def write_file(self, path_name, content_list): for content in content_list: with open(path_name, "a", encoding="UTF-8") as f: f.write(json.dumps(content, ensure_ascii=False)) f.write("\n") def run(self): start_url = 'https://weibo.com/u/5644764907"第{}条微博相关评论.txt".format(i * 45 + j + 1) all_count, comment_info_list = self.parse_comment_info(all_url[j]) self.write_file(path_name, comment_info_list) for num in range(1, 10000): if num * 15 < int(all_count) + 15: comment_url = all_url[j] + "&page={}".format(num + 1) print(comment_url) try: count, comment_info_list = self.parse_comment_info(comment_url) self.write_file(path_name, comment_info_list) except Exception as e: print("Error:", e) time.sleep(60) count, comment_info_list = self.parse_comment_info(comment_url) self.write_file(path_name, comment_info_list) del count time.sleep(0.2) print("第{}微博信息获取完成!".format(i * 45 + j + 1)) if __name__ == '__main__': weibo = Weibospider() weibo.run()
下一篇:删除pycharm鼠标右键快捷键打开项目的操作
几个月来,英特尔、微软、AMD和其它厂商都在共同推动“AI PC”的想法,朝着更多的AI功能迈进。在近日,英特尔在台北举行的开发者活动中,也宣布了关于AI PC加速计划、新的PC开发者计划和独立硬件供应商计划。
在此次发布会上,英特尔还发布了全新的全新的酷睿Ultra Meteor Lake NUC开发套件,以及联合微软等合作伙伴联合定义“AI PC”的定义标准。
在此次发布会上,英特尔还发布了全新的全新的酷睿Ultra Meteor Lake NUC开发套件,以及联合微软等合作伙伴联合定义“AI PC”的定义标准。